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International Joint Conference on Neural Networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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Improved harris hawks optimization algorithm based on collaborative quantization 6

Improved harris hawks optimization algorithm based on collab...

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6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Li, Yan Zhang, Xiang Qian, Qian Pan, Jiawen Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan650500 China Logistic Service Group Yunnan University Kunming Yunnan650500 China

ISBN: (纸本)9781510656635

Harris hawk optimization algorithm (HHO) is one of the population-based algorithms proposed by in *** has received great attention from researchers. However, HHO algorithm still has some problems, such as the exploitation ability is too large compared with the exploration ability, which leads to low optimization accuracy, slow convergence speed and so on. Therefore, the collaborative strategy and quantization strategy are introduced. With the support of the two strategies, the algorithm can avoid falling into local extremum in the early phase of iteration and improve its optimization accuracy at the late phase of iteration. Through the test of four representative functions, compared with the other three basic algorithms, the proposed algorithm greatly improves the optimization accuracy and convergence speed of the optimal solution, and the ability to find global extremum is greatly improved. © 2022 SPIE.

关键词： Optimization

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Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder

Cross-modal retrieval based on Attention Embedded Variationa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Indoor Staircase Detection for Supporting Security Systems in Autonomous Smart Wheelchairs Based on Deep Analysis of the Co-Occurrence Matrix

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SSRN 2023年

作者： Utaminingrum, Fitri Wahyudi, Slamet Karim, Corina Johan, Ahmad Wali Satria Bahari Shih, Timothy K. Lin, Chih-Yang Computer Vision Research Groups Faculty of Computer Science Brawijaya University Malang65145 Indonesia Mechanical Department Brawijaya University Malang65145 Indonesia Mathematics Department Brawijaya University Malang65145 Indonesia Department of Informatics Institut Teknologi Telkom Purwokerto Surabaya65145 Indonesia Department of Computer Science and Information Engineering National Central University Taoyuan City32001 Taiwan Computer Vision and Interactive Technology Department of Electrical Engineering Yuan-Ze University Taoyuan City32003 Taiwan

Detection of descending stairs and floors is important in implementing autonomous systems in smart wheelchairs. Suppose the obstacle detection system applied to wheelchairs cannot properly detect the descending stairs. In that case, it will have fatal consequences for its users, one of which is an injury or even the worst impact can result in death. Hence, we need an algorithm that has good accuracy in detecting obstacles in descending stairs and a fast-computing time so that the system's response for braking on a wheelchair can be made immediately. In this study, we use the Gray Level Co-Occurrence Matrix (GLCM) method for the basis of feature ***, we analyzed the performance of several types of binary classification methods, including Naive Bayes, Decision Tree, Logistic Regression, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). Based on the results obtained, the Decision Tree has the highest accuracy value compared to the others. The Accuracy value is 94%, and the computing time is 0.01299 seconds. These results were obtained in a combination of the GLCM method, the distance is equal to 2, and the angle is 45o. © 2023, The Authors. All rights reserved.

关键词： Stairs

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MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding

MFFLEN: Multi-Label Text Classification Based on Multi-Featu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiliang Gu Shuo Zhao Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Shandong Engineering Research Center of Big Data Applied Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

To address the challenges associated with insufficiently extracting and utilizing features at different levels, overlooking the connection between label meanings and text, and facing problems of over-compression or information loss when extracting global information using recurrent neural networks in the field of multi-label text categorization, this paper introduces an innovative model known as MFFLEN (MultiFeature Fusion and Label Embedding Neural Network). First, a back-translated enhanced label set is constructed by back-translated splicing enhancement of the original label set. This set, together with the text, is then input into the embedding layer, which consists of the pre-trained model of bert-baseChinese, thus establishing the initial connection between the text and the labels within the same vector space. Then, to comprehensively extract multi-level semantic features, the model uses a convolutional layer to extract local features and an embedding layer to extract sentence-level features. A bidirectional attention embedded GRU (BAE-GRU) layer is used to extract hybrid finegrained features, which are then fed into the attention layer to further extract hybrid labeled features based on labeling information. Finally, these three different types of features are fused and multi-label text classification results are obtained using a classifier. The experiments proved that the MFFLEN model achieved 73.82% and 88.44% macro-F1 and 88.00% and 88.86% micro-F1 on the two datasets CAIL 2018 Small and CAIL 2018 Split, respectively, which is better than other baseline models.

关键词： Measurement Recurrent neural networks Splicing Text categorization Semantics Multi label classification Logic gates Feature extraction Vectors Data mining

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Semantic Relation-Based Cross Attention Network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention Network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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LTGPv2: Rethinking local track geometry for Track-to-Track Association 5

LTGPv2: Rethinking local track geometry for Track-to-Track A...

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5th CAA International Conference on Vehicular Control and Intelligence, CVCI 2021

作者： Zou, Ke Zhou, Tianle Zhou, Zou Ren, Kai Li, Yanhong Jiang, Xi Yuan, Xuedong Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University College of Computer Science Chengdu China Guilin University of Electronic Technology School of Information and Communication Guilin China

ISBN: (纸本)9781665408462

Track-to-track association (T2TA) is an essential part in situational awareness of advanced driving assistant systems. The accuracy of track-to-track association methods may degrade by missed detections and measurement bias. Although the local track geometry preservation algorithm has been proposed to improve the performance of T2TA, it may be affected as the target detection decreases. In this study, we proposed second version of the local track geometry preservation algorithm, called LTGPv2, which introduces another local track structure descriptor for T2TA. The local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, and are fitted to the corresponding local tracks of the other sensor. The T2TA problem is formulated as a maximum likelihood estimation problem with two local track geometry constraints to avoid the degradation of T2TA performance caused by missing detection. Then, an expectation-maximization (EM) algorithm is applied to solve it. Simulation results demonstrate that LTGPv2 obtain better performance than the state-of-the-art methods. © 2021 IEEE.

关键词： Maximum likelihood estimation

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From early biological models to CNNs: do they look where humans look?

From early biological models to CNNs: do they look where hum...

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International Conference on Pattern Recognition

作者： Marinella Iole Cadoni Andrea Lagorio Enrico Grosso Tan Jia Huei Chan Chee Seng Computer Vision Laboratory University of Sassari Sassari Italy Faculty of Computer Science and Information Technology Center of Image and Signal Processing University of Malaya

Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN's human similarity and classification performance.

关键词： Visualization Correlation Databases Neural networks Semantics Pipelines Visual systems

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HLS-based Hardware Acceleration on the Zynq SoC: A Real-Time Face Detection and Recognition System 9

HLS-based Hardware Acceleration on the Zynq SoC: A Real-Time...

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9th IEEE International Conference on sciences of Electronics, Technologies of information and Telecommunications, SETIT 2022

作者： Kortli, Yassin Gabsi, Souhir Jridi, Maher Lew Yan Voon, Lew F. C. Atri, Mohamed University of Monastir Electronic and Micro-Electronic Laboratory Faculty of Sciences of Monastir Tunisia L@Bisen Vision-AD Team Yncrea Ouest Carquefou44470 France Universite de Bourgogne Laboratoire ImViA France King Khalid University College of Computer Science Abha61421 Saudi Arabia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Face detection and recognition system (FDRS) is widely used in modern biometric security systems. In this paper, we implement a new model of FDRS to identify human faces under different poses. Particularly, we propose the Viola-Jones detector to locate and detect human faces and the VanderLugt correlator (VLC) technique for identification. We propose two implementations based on Zync SOC. A set of experiments has been considered using the PHPID database where the faces ranging from -30° to +30°. For more details, see [4]. © 2022 IEEE.

关键词： System-on-chip

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： computer vision Fuses Image matching Semantics Feature extraction Natural language processing

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